Method for locating an interproximal tooth region

ABSTRACT

A method for locating one or more interproximal tooth regions in a digital tooth image. The method can be executed at least in part on data processing hardware. The method includes generating the digital tooth image from a fluorescence image of one or more teeth and a reflectance image of the one or more teeth, so as to combine image data from the fluorescence and reflectance images. The digital tooth image has intensity values for pixels corresponding to the one or more teeth and background. The method identifies one or more tooth regions by processing the digital tooth image and locates the one or more interproximal tooth regions according to the one or more identified tooth regions.

CROSS REFERENCE TO RELATED APPLICATIONS

Reference is made to U.S. Ser. No. 12/578,806 entitled METHOD FOREXTRACTING A CARIOUS LESION AREA to Yan et al, filed on Oct. 14, 2009,commonly assigned and incorporated herein by reference.

Reference is made to U.S. Ser. No. 12/578,795 entitled METHOD FORIDENTIFYING A TOOTH REGION to Yan et al, filed on Oct. 14, 2009,commonly assigned and incorporated herein by reference.

FIELD OF THE INVENTION

The invention relates generally to the field of dental imaging, and inparticular to a method for early detection of caries. More specifically,the invention relates to a method for locating an interproximal toothregion in a tooth image captured using fluorescence and back-scatteringof light.

BACKGROUND OF THE INVENTION

While there have been improvements in detection, treatment andprevention techniques, dental caries remains a prevalent conditionaffecting people of all age groups. If not properly and promptlytreated, caries could lead to permanent tooth damage and even to loss ofteeth.

Traditional methods for caries detection include visual examination andtactile probing with a sharp dental explorer device, often assisted byradiographic (x-ray) imaging. Detection using these methods can besomewhat subjective, varying in accuracy due to many factors, includingpractitioner expertise, location of the infected site, extent ofinfection, viewing conditions, accuracy of x-ray equipment andprocessing, and other factors. There are also hazards associated withconventional detection techniques, including the risk of damagingweakened teeth and spreading infection with tactile methods as well asexposure to x-ray radiation. By the time a caries condition is evidentunder visual and tactile examination, the disease is generally in anadvanced stage, requiring a filling and, if not timely treated, possiblyleading to tooth loss.

In response to the need for improved caries detection methods, there hasbeen considerable interest in improved imaging techniques that do notemploy x-rays. One method employs fluorescence wherein teeth areilluminated with high intensity blue light. This technique, sometimestermed quantitative light-induced fluorescence (QLF), operates on theprinciple that sound, healthy tooth enamel yields a higher intensity offluorescence under excitation from some wavelengths than doesde-mineralized enamel that has been damaged by caries infection. Thecorrelation between mineral loss and loss of fluorescence for blue lightexcitation is then used to identify and assess carious areas of thetooth. A different relationship has been found for red light excitation,a region of the spectrum for which bacteria and bacterial by-products incarious regions absorb and fluoresce more pronouncedly than do healthyareas.

Some references are noted which relate to optical detection of caries.

U.S. Pat. No. 4,515,476 (Ingmar) describes the use of a laser forproviding excitation energy that generates fluorescence at some otherwavelength for locating carious areas in an image.

U.S. Pat. No. 6,231,338 (de Josselin de Jong et al.) describes animaging apparatus for identifying dental caries using fluorescencedetection.

U.S. Patent Application Publication No. 2004/0240716 (de Josselin deJong et al.) describes methods for improved image analysis for imagesobtained from fluorescing tissue.

U.S. Pat. No. 4,479,499 (Alfano) describes a method for usingtransillumination to detect caries based on the translucent propertiesof tooth structure.

Among products for dental imaging using fluorescence behavior is the QLFClinical System from Inspektor Research Systems BV, Amsterdam, TheNetherlands. The Diagnodent Laser Caries Detection Aid from KaVo DentalCorporation, Lake Zurich, Ill., USA, detects caries activity monitoringthe intensity of fluorescence of bacterial by-products underillumination from red light.

U.S. Patent Application Publication No. 2004/0202356 (Stookey et al.)describes mathematical processing of spectral changes in fluorescence inorder to detect caries in different stages with improved accuracy.Acknowledging the difficulty of early detection when using spectralfluorescence measurements, the '2356 Stookey et al. disclosure describesapproaches for enhancing the spectral values obtained, effecting atransformation of the spectral data that is adapted to the spectralresponse of the camera that obtains the fluorescent image.

While the described methods and apparatus are intended for non-invasive,non-ionizing imaging methods for caries detection, there is room forimprovement. A recognized drawback with existing techniques that employfluorescence imaging relates to image contrast. The image provided byfluorescence generation techniques such as QLF can be difficult toassess due to relatively poor contrast between healthy and infectedareas. As noted in the '2356 Stookey et al. disclosure, spectral andintensity changes for incipient caries can be very slight, making itdifficult to differentiate non-diseased tooth surface irregularitiesfrom incipient caries.

With fluorescence techniques, the image contrast that is obtainedgenerally corresponds to the severity of the condition. Accurateidentification of caries using these techniques often requires that thecondition be at a more advanced stage, beyond incipient or early caries,because the difference in fluorescence between carious and sound toothstructure is very small for caries at an early stage. In such cases,detection accuracy using fluorescence techniques may not show markedimprovement over conventional methods. Because of this shortcoming, theuse of fluorescence effects appears to have some practical limits thatprevent accurate diagnosis of incipient caries. As a result, a cariescondition may continue undetected until it is more serious, requiring afilling, for example.

Detection of caries at very early stages is of particular interest forpreventive dentistry. As noted previously, conventional techniquesgenerally fail to detect caries at a stage at which the condition can bereversed. As a general rule of thumb, incipient caries is a lesion thathas not penetrated substantially into the tooth enamel. Where such acaries lesion is identified before it threatens the dentin portion ofthe tooth, remineralization can often be accomplished, reversing theearly damage and preventing the need for a filling. More advancedcaries, however, grows increasingly more difficult to treat, most oftenrequiring some type of filling or other type of intervention.

To take advantage of opportunities for non-invasive dental techniques toforestall caries, it is necessary that caries be detected at the onset.In many cases, as is acknowledged in the '2356 Stookey et al.disclosure, this level of detection has been found to be difficult toachieve using existing fluorescence imaging techniques, such as QLF. Asa result, early caries can continue undetected, so that by the timepositive detection is obtained, the opportunity for reversal usinglow-cost preventive measures can be lost.

In commonly-assigned U.S. Patent Application Publication No.2008/0056551, a method and apparatus that employs both the reflectanceand fluorescence images of the tooth is used to detect caries. It takesadvantage of the observed back-scattering, or reflectance, for incipientcaries and in combination with fluorescence effects, to provide animproved dental imaging technique to detect caries. The technique,referred to as Fluorescence Imaging with Reflectance Enhancement (FIRE),helps to increase the contrast of images over that of earlierapproaches, and also makes it possible to detect incipient caries atstages when preventive measures are likely to take effect.Advantageously, FIRE detection can be accurate at an earlier stage ofcaries infection than has been exhibited using existing fluorescenceapproaches that measure fluorescence alone. The application describes adownshifting method to generate the FIRE image.

Commonly-assigned copending PCT/CN2009/000078, entitled METHOD FORDETECTION OF CARIES describes a morphological method for generating aFIRE image with reduced sensitivity to illumination variation.

Quantification of caries based on a digital image of a tooth such as afluorescence image provides numerical information on the severity oflesion regions and can help dentists make and carry out treatment plans.It can be a useful tool in the longitudinal monitoring of caries fordentists to observe the evolution of each lesion area over time. U.S.Patent Application Publication No. 2004/0240716 has disclosed somemethods for quantification of caries; however, the disclosed methodsgenerally require manual extraction of lesion regions from sound toothareas of the image by the user, and they are based on fluorescence-onlyimages. Manual extraction of lesion regions from the image presents someproblems. The extraction process is slow, requiring the user to makemany mouse clicks or to draw lines on the images to indicate theboundary of a lesion region. Secondly, manual extraction requiresconsiderable caries diagnostic experience on the part of the user and isgenerally subjective. In addition, fluorescence-only images displayincipient caries at relatively low contrast, further adding difficultyto the manual lesion extraction process. Therefore, in the disclosedmethods, only compromised caries quantification results are achieved, atbest.

Commonly-assigned copending U.S. patent application Ser. No. 12/487,729entitled METHOD FOR QUANTIFYING CARIES describes an improved method forquantifying caries. This method, however, partly relies on a key step ofextracting a lesion area in a tooth image, which further partiallyrelies on a key sub-step of locating an interproximal tooth region.

A benefit of locating an interproximal tooth region is noted that cariestends to occur at the interproximal regions more than at other regionsof the tooth. Additionally, it is difficult to detect or locate cariesthat are at the interproximal tooth regions. There exist known methodsfor determining interproximal regions in X-ray dental images usingprojection and active contour techniques, as discussed by Jain et al.,“Matching of dental x-ray images for human identification”, PatternRecognition Vol. 37, pp. 1519-1532, 2004, and Chen et al., “Toothcontour extraction for matching dental radiographs”, ICPR 2004. However,these known methods are not applicable to a fluorescence image,reflectance image, or FIRE image because of significant differences inimage content and characteristics. Thus, it can be seen that there is aneed for an improved method for locating an interproximal tooth regionin a tooth image, particularly in a FIRE image or fluorescence image ofa tooth.

SUMMARY OF THE INVENTION

An object of the present invention is to provide a method for locatinginterproximal tooth regions in a digital image of a tooth, useful forextracting a carious lesion area and quantifying caries.

Another object of the present invention is to provide a method forlocating interproximal tooth regions in a fluorescence image or FIREimage of a tooth.

A feature of the present invention is that interproximal tooth regionsare automatically extracted in a FIRE image, the high contrast in theFIRE image providing improved sensitivity and accuracy for theidentification of caries.

An advantage of the present invention is that interproximal toothregions in tooth images are located without user intervention, thusproviding an efficient workflow in caries extraction, identification,and monitoring.

These objects are given only by way of illustrative example, and suchobjects may be exemplary of one or more embodiments of theinvention.—invention may occur or become apparent to those skilled inthe art. The invention is defined by the appended claims.

According to one aspect of the invention, there is provided a method forlocating interproximal tooth regions in a digital tooth image, executedat least in part on data processing hardware, the method comprisinggenerating the digital tooth image by obtaining a fluorescence image ofone or more teeth, obtaining a reflectance image of the one or moreteeth, and combining image data from the fluorescence and reflectanceimages, wherein the digital tooth image comprises intensity values forpixels corresponding to the one or more teeth and background;identifying one or more tooth regions by processing the digital toothimage; and locating the one or more interproximal tooth regionsaccording to the one or more identified tooth regions.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other objects, features, and advantages of theinvention will be apparent from the following more particulardescription of the embodiments of the invention, as illustrated in theaccompanying drawings. The elements of the drawings are not necessarilyto scale relative to each other.

FIG. 1 shows a method for quantifying caries comprising five steps usingthe present invention.

FIGS. 2A, 2B, 2C show illustratively a typical reflectance image, afluorescence image, and a FIRE image, respectively.

FIG. 2D is a view showing the process for combining dental image data togenerate a FIRE image.

FIG. 3A shows an embodiment of a digital image generation step.

FIG. 3B shows a tooth region in a FIRE image having a lesion areaidentified in an extraction step.

FIG. 3C shows an embodiment of a step for extracting a lesion area fromsound tooth regions using the present invention.

FIG. 3D shows a FIRE image with a dilated line in a sound tooth area,and a lesion segmentation border separating a sound tooth area and alesion area after a sound region identification step.

FIG. 3E shows an embodiment of an intensity reconstruction step using abilinear interpolation.

FIG. 4A shows a binary image of three teeth.

FIG. 4B shows a contour line formed from a fan of ray lines cast outwardfrom the origin point.

FIG. 4C shows determined internal and external markers.

FIG. 4D is an illustration of the marker-controlled watershed result.

FIG. 4E is an illustration of interlines between adjacent teeth.

FIG. 5A shows a binary image of three teeth similar to FIG. 4A.

FIG. 5B shows a distance image Idist formed from a distancetransformation on the image of FIG. 5A.

FIG. 5C shows seed points in seeded areas.

FIG. 5D shows internal and external markers.

FIG. 5E is an illustration of interlines after the marker-controlledwatershed and distance transform processing.

FIG. 6A shows a method for quantification of caries using the presentinvention.

FIG. 6B shows another method for quantifying caries using the presentinvention.

DETAILED DESCRIPTION OF THE INVENTION

The following is a detailed description of the preferred embodiments ofthe invention, reference being made to the drawings in which the samereference numerals identify the same elements of structure in each ofthe several figures.

Reference is made to commonly-assigned copending U.S. patent applicationSer. No. 12/487,729 filed on Jun. 19, 2009 and entitled METHOD FORQUANTIFYING CARIES, by Liangliang Pan et al.

Reference is made to PCT/CN2009/000078, filed on Jan. 20, 2009, entitledMETHOD FOR DETECTION OF CARIES, by Wei Wang et al.

Reference is made to U.S. Patent Application Publication No.2008/0056551, published Mar. 6, 2008, entitled METHOD FOR DETECTION OFCARIES, by Wong et al.

Reference is made to U.S. Patent Application Publication No.2008/0063998, published Mar. 13, 2008, entitled APPARATUS FOR CARIESDETECTION, by Liang et al.

Reference is made to U.S. Patent Application Publication No.2008/0170764, published Jul. 17, 2008, entitled SYSTEM FOR EARLYDETECTION OF DENTAL CARIES, by Burns et al.

Reference is made to U.S. Patent Publication No. 2007/0099148, publishedon May 3, 2007, entitled METHOD AND APPARATUS FOR DETECTION OF CARIES,by Wong et al.

This invention includes calculation steps that are performed by dataprocessing hardware that is provided with instructions for image dataprocessing. Because such image manipulation systems are well known, thepresent description is directed more particularly to algorithms andsystems that execute the method of the present invention. Other aspectsof such algorithms and systems, and data processing hardware and/orsoftware for producing and otherwise processing the image signals may beselected from such systems, algorithms, components and elements known inthe art. Given the description as set forth in the followingspecification, software implementation lies within the ordinary skill ofthose versed in the programming arts.

The stored instructions of such a software program may be stored in acomputer readable storage medium, which may comprise, for example:magnetic storage media such as a magnetic disk or magnetic tape; opticalstorage media such as an optical disc, optical tape, or machine readablebar code; solid state electronic storage devices such as random accessmemory (RAM), or read only memory (ROM); or any other physical device ormedium employed to store a computer program. Using such software, thepresent invention can be utilized on a data processing hardwareapparatus, such as a computer system or personal computer, or on anembedded system that employs a dedicated data processing component, suchas a digital signal processing chip. Output resulting from executing themethod of the present invention is provided as digital data for imagedisplay, for storage in an electronic memory, and, optionally, for useby other diagnostic image processing utilities.

In this disclosure, the word “intensity” is used to refer to lightlevel, and is also broadly used to refer to the value of a pixel in adigital image. The terms “interproximal region” and “interproximal toothregion” are equivalent and are used interchangeably in the presentdisclosure.

Where they are used, the terms “first”, “second”, and so on, do notnecessarily denote any ordinal or priority relation, but may be simplyused to more clearly distinguish one element from another.

The term “water basin” as used herein is a term of art used to describea structure that is identified and used in executing a marker-controlledwatershed transformation in the imaging arts. The term “catchment basin”is sometimes used in the same way. References in this disclosure to“water basin” refer to this imaging arts construct.

Referring to FIG. 1, a method for quantifying caries, executed at leastin part on data processing hardware such as computer hardware, comprisesa step 110 of generating a digital image of a tooth, the imagecomprising actual intensity values for a region of pixels correspondingto the tooth, gum, and background; a step 120 of extracting a lesionarea from sound tooth regions by identifying tooth regions, extractingsuspicious lesion areas, and removing false positives; a step 130 ofidentifying a sound region that is adjacent to the extracted lesionarea; a step 140 of reconstructing intensity values for tooth tissuewithin the lesion area according to values in the adjacent sound region;and a step 150 of quantifying the condition of the caries using thereconstructed intensity values and intensity values from the lesionarea. Note that the phrase “extracting a lesion area,” as usedthroughout this application, means identifying at least one lesion areain a digital tooth image.

FIGS. 2A, 2B, and 2C show illustratively a typical reflectance image167, a fluorescence image 168, and a FIRE image 169, respectively, of atooth surface including a sound tooth area 164 and an early lesion area(or caries region) 162. Generally, in a reflectance image, such as awhite light reflectance image, the intensity of early caries regions ishigher than that of their surrounding sound areas. In contrast, in afluorescence image, such as one obtained under blue excitation light,the intensity of caries regions is lower than that of their surroundingsound areas because of the fluorescence loss in caries regions. A FIREimage is obtained through subtracting regional maxima and dome regionsof the reflectance image from the fluorescence image. As a result, theFIRE image has a similar appearance as a fluorescence image because bothhave lower intensity values in a lesion area than in a surrounding soundarea. However, the FIRE image has higher contrast than a fluorescenceimage, making it potentially more sensitive in detecting caries. Itshould be noted that other images that are generated by combining imagedata for the fluorescence and reflectance images can also be used forsubstituting the FIRE image.

FIG. 2D corresponds to FIG. 5 of commonly-assigned copending U.S. PatentApplication Publication No. 2008/0056551 (Wong et al.), entitled METHODFOR DETECTION OF CARIES. This figure shows that a FIRE image 169 isformed by combining the fluorescence image 168 with the reflectanceimage 167 through a processing apparatus 180.

In the image processing field, there are many well known methods used toextract features from images, including but not limited to threshold,top-hat, and morphological grayscale reconstruction techniques (see LucVincent, “Morphological grayscale reconstruction in image analysis:applications and efficient algorithms”, IEEE Transactions on ImageProcessing, Vol. 2, No. 2, pp. 176-201, 1993). However, not everytechnique is suitable for segmenting lesions from an image of a tooth.Teeth images have many characteristics that pose challenges for doingautomatic lesion extraction. For example, a tooth image has no flatbackground (sound tooth areas are the background of the target caries),the caries sites have no fixed sizes and shapes, and surface contour andcurvature of teeth cause uneven illumination, resulting in intensityvariation across the tooth image. The present invention overcomes thesedifficulties by employing a combination of different image processingtechniques that address the various problems specific to automaticprocessing of teeth images.

In the following sections, steps for quantifying caries are describedwith reference to FIGS. 3A through 5E. The methods for locating aninterproximal tooth region from the digital image data according to thepresent invention, needed for the image processing steps of quantifyingcaries, extracting a carious lesion region, and removing falsepositives, are also described.

Step 110 of Generating a Digital Image of a Tooth

FIG. 3A shows one embodiment of step 110 of generating a digital imageof a tooth, comprising steps of obtaining a fluorescence image,obtaining a reflectance image, and combining image data for thefluorescence and reflectance images to generate an image such as a FIREimage. Details of how the fluorescence and reflectance images areobtained are described in U.S. Patent Application Publication No.2008/0063998, published Mar. 13, 2008, entitled APPARATUS FOR CARIESDETECTION, by Liang et al. According to this embodiment, the digitalimage of the tooth is a FIRE image 169, which is formed by combining thefluorescence image 168 with the reflectance image 167 through aprocessing apparatus 180 as shown in FIG. 2D.

The details of generating the FIRE image have been disclosed in thecommonly-assigned copending PCT/CN2009/000078, entitled METHOD FORDETECTION OF CARIES. Steps of generating the FIRE image include thefollowing.

1. Obtaining a reflectance image, and then converting the reflectanceimage into a gray reflectance image with an intensity value of Iwgreen.The gray reflectance image can be the green channel of the reflectanceimage. This gray reflectance image is treated as a mask, and it has anintensity value of Imask=Iwgreen. In one example, the reflectance imageis a white light reflectance image. The white light can be emitted fromone or more white LEDs.

2. Generating a marker with an intensity value of Imarker according tothe following formula,Imarker=Imask−hdome,where hdome, representing the height of a dome in the gray reflectanceimage, is a fixed value and is empirically selected based on theintensity values of a plurality of gray reflectance teeth imagesobtained. In one inventive example, hdome is 50.

3. Generating a reconstructed image having an intensity value ofIreconstructed through morphological grayscale reconstruction, whichtakes Imask and Imarker as input (see the Luc Vincent article, citedearlier).

4. Generating an image of regional maxima and dome regions of the grayreflectance image. This image, corresponding to the suspicious cariesregions, has an intensity valueIhdome=Imask−Ireconstructed.

5. Generating a FIRE image with an intensity valueI _(FIRE) =I _(Fluo) −Ihdome,where I_(FIRE) and I_(Fluo) are the intensity values of the greenchannel of the generated FIRE image and the obtained fluorescence image,respectively. The generated FIRE image can be displayed as a color imageby combining I_(FIRE) with the red and blue channels of the fluorescenceimage. In one example, the fluorescence image is one obtained under blueexcitation light. The blue light can be emitted from one or more blueLEDs. The FIRE image is the digital image used for subsequent imageprocessing steps.

Another embodiment of step 110 of generating a digital image of a toothcomprises a step of obtaining a fluorescence image. The fluorescenceimage is the digital image used for subsequent image processing steps.

Step 120 of Extracting a Lesion Area from Sound Tooth Regions

Generally, a digital image of a tooth can be classified into threegroups of regions: 1) gum, 2) tooth, and 3) other background. Cariesdetection only needs to be performed inside tooth regions 165.

Referring to FIG. 3B, inside the tooth region 165 is a lesion area 162,a surrounding sound tooth area 164, and segmentation border 163 thatseparates the two areas. Methods for identifying tooth region 165,lesion area 162, surrounding sound tooth area 164, and segmentationborder 163 are described below.

FIG. 3C shows an embodiment of step 120 for extracting a lesion area 162from tooth regions 165 in a digital image of a tooth according to anembodiment of the present invention. Step 120 is performed automaticallywithout a need for user input. Specifically, step 120 includes sub-stepsof identifying the tooth regions 165, extracting one or more suspiciouslesion areas, and removing false positives. These sub-steps includedetails specific to tooth images, as discussed below.

Since some image processing work is done on a certain channel of a colorimage, for convenience, the following terms Iwred, Iwgreen, Iwblue,Ibred, Ibgreen, Ibblue, Ifred, Ifgreen, and Ifblue are used to representthe intensity values of the pixels in the red, green, and blue channelsof the reflectance, fluorescence, and FIRE images, respectively. And inorder to remove the impact of illumination level, intensity values ofboth reflectance and fluorescence images are adjusted to a range between0 and 150, where 0 and 150 correspond to minimum and maximum intensityvalues.

As discussed, similar to the fluorescence image, the FIRE image hashigher green intensity values inside normal/sound tooth areas than incaries and other background areas. Consequently, an adapted thresholdtechnique is preferably used on a fluorescence or FIRE image to separatethe tooth region, which contains both normal/sound tooth areas andcaries areas, from the gum and other background.

Sub-Step of Identifying Tooth Regions 165

According to one embodiment, tooth regions 165 are identified from thedigital tooth image as follows. In this embodiment and other embodimentsthroughout the disclosure, grayscale versions of both the fluorescenceand reflectance images are used, the grayscale images being generatedfrom one channel of their respective color images, such as the greenchannel, or from a mixing of the three channels using methods well knownin the image processing art. For illustrative purposes, the embodimentis described below using the green channels of the fluorescence andreflectance images, Ibgreen and Iwgreen, respectively.

Threshold images are generated from Ibgreen and Iwgreen by selectingintensity values higher than some predetermined threshold values c1 andc2, for example, 10 and 30, respectively. Secondly, the intersectionregions of the two threshold images are taken as the preliminary toothregions image Iroi0. Thirdly, a reference binary image Irefroi isobtained by thresholding the image Ibgreen with a threshold value c3higher than the one used in generating Iroi0, such as 30. And lastly, arefined tooth regions 165 image, Iroi, is generated by choosing theregions that are in Iroi0 and connected to the objects in Irefroi. Theabove four steps increase the accuracy of selecting tooth regions 165 ascompared to thresholding just the FIRE or fluorescence image. Therefined tooth regions 165 image is then used in the following sub-stepsof extracting suspicious lesion areas and removing false positives.

In an alternative embodiment, thresholding technique is applied to thefluorescence or FIRE image to determine tooth regions 165. Thisembodiment helps to provide simpler and faster processing.

Sub-Step of Extracting a Suspicious Lesion Area

In a FIRE image (Ifgreen), there is a definite morphologicalcharacteristic for caries, that is, the intensity of region of caries162 is lower than that of the surrounding sound tooth area 164. Thischaracteristic is used to detect and segment the suspicious caries areasbased on mathematical morphology theory.

In one embodiment, a marker-controlled watershed based method is adaptedto detect and segment the suspicious caries areas. The key to thismethod is to determine internal and external markers for the targetobjects. According to one example, the internal markers are determinedwith the morphological grayscale reconstruction technique. The sametechnique has also been used for generating a FIRE image as discussedabove.

To determine internal markers with the morphological gray-scalereconstruction method, the regional basins Ihbasin are first detected;they correspond to the target regions of caries because they have lowerintensity than surrounding sound areas. Then, the internal markers areobtained by thresholding Ihbasin with a fixed value, for example, 50.Note that the fixed value can be adjusted according to detectionsensitivity requirement. The internal markers are the regions insidewhich the intensities of Ihbasin are higher than the given thresholdvalue.

To obtain the external markers, a binary image is first formed from theinternal markers, wherein the pixel value of the binary image is 1 for apixel inside internal markers and is 0 otherwise. Then a distancetransformation (DT), mapping each image pixel onto its shortest distanceto the target objects, is applied to the binary image to generate a DTimage (see “Sequential operations in digital picture processing”, J.ACM. 13, 1966, by Rosenfeld, A. and Pfaltz, J. and “2D Euclideandistance transform algorithms: a comparative survey”, ACM computingsurveys 40, 2008, by Ricardo Fabbri, Luciano Da F. Costa, Julio C.Torelli and Odemir M. Bruno). The ridge lines that are composed of thepixels with local maximal values in the DT image and located between theinternal markers are taken as the external markers.

The gradient image of Ifgreen is calculated with the Sobel operator. TheSobel operator is an image processing function well known to thoseskilled in the image processing/pattern recognition art; a descriptionof it can be found in Pattern Classification and Scene Analysis, Duda,R. and Hart, P., John Wiley and Sons, 1973, pp. 271-272.

With the internal and external markers and the gradient image identifiedor determined, marker-controlled watershed transformation is thenapplied to generate a contour of the target regions of caries 162directly. A description of the marker-controlled watershedtransformation can be found in “Morphological grayscale reconstructionin image analysis: applications and efficient algorithms”, IEEETransactions on Image Processing, Vol. 2, pp. 176-201, 1993, by LucVincent.

In another embodiment, a method based on morphological bottom-hatoperation along with the multi-resolution and surface reconstructiontechniques is adapted to detect and segment the suspicious caries areas.According to this embodiment, a bottom-hat operation is first applied toIfgreen to produce an original bottom-hat image with an intensity valueof Ibothat. Then a multi-resolution strategy is adapted to enabledetection of caries with different sizes. According to this strategy,the original bottom-hat image is down-sampled to form one or morereduced-resolution bottom-hat images, such as 2×-down sampled image and4×-down sampled image. Given a 2-Dimensional shaped structure elementwith a fixed size, for example, a disk with a radius of 10 pixels, themorphological bottom hat is then applied to the images with differentresolutions (that is, original bottom-hat image, 2×-down sampledbottom-hat image, 4×-down sampled bottom-hat image, etc.). Note that the2-Dimensional structure element can take other shapes. The size of thestructure element, for example, the radius of the disk, can be adjustedaccording to the image resolution or the size of the target objects. Foreach of the obtained multi-resolution bottom-hat images, according tothe statistic of the intensity value inside the corresponding toothregions, a threshold value Ithres is calculated asIthres=Imean+w*Istd,where w is the weighting parameter determined experimentally, and Imeanand Istd are the mean and standard deviation of intensity values,respectively. Applying a threshold operation to each of themulti-resolution bottom-hat images, a binary image is obtained, insidewhich the regions with a nonzero value are the initial suspicious cariesareas in the image with corresponding resolution. After interpolatingeach of the binary images back to the original resolution to produceinterpolated images, the union of all the interpolated images is takenas the initial suspicious lesion areas.

Since unable to use an infinite number of resolutions, and the size andshape of the structure elements are not the same as those of the targetregions of caries 162, the initial suspicious caries areas are usuallynot the optimal results.

However, by using a small value of the weighting parameter w, the targetcaries areas can be included inside the initial suspicious caries areaswith high confidence. In one example, the weighting parameter w is 1.0,0.5, and 0 for the original, 2×-down sampled, and 4×-down sampledimages, respectively. Certainly, the weighting parameter w can beadjusted according to practical requirements.

The normal intensity values (i.e., intensity values of the areas beforethe development of caries) inside the initial suspicious caries areascan be further estimated according to those outside the initialsuspicious caries areas. According to one example, the intensityestimation is a surface reconstruction processing, generatingIreconstructed, where intensity is taken as a topological surface.Subtracting the original image Ifgreen from the reconstructed imageIreconstructed, a difference image Idiff is obtained. Because theintensity values inside the caries areas are lower than those of thenormal/sound tooth areas, and the change between parts inside the normalor sound tooth areas is not as much as that between the caries and thenormal/sound tooth areas, the regions with larger change in intensityvalues (for example, >7, which can be adjusted according to the requireddetection sensitivity) are taken as the refined suspicious caries areas.

While the morphological grayscale reconstruction technique could also beused to detect regional maxima-dome of a certain height or regionalminima-basin of a certain depth in a grayscale image, it is not assuitable as the embodiments discussed above to extract caries lesion inteeth image. This is because different caries areas have differentcontrast with respect to their surrounding areas. Thus, differentregional extrema heights or depths are needed to suit different imagesor different caries infections. After all, the height or depth is stilla global parameter. Additionally, the morphological grayscalereconstruction is more difficult to be implemented and is slower thanthe morphological bottom-hat method.

While a conventional top/bottom hat method might also be considered foruse to detect regional maxima dome or minima basin regions, the methodalso is unsuitable for extracting a caries lesion because it isdifficult to determine the size of the structure element. This is unlikethe morphological bottom-hat method discussed in this application,which, when used along with the multi-resolution and surfacereconstruction techniques, successfully overcomes the problem ofdetermining the size of the structure element.

Sub-Step of Removing False Positives

Based on experimental results, most occurrences of false positives canbe grouped into two categories: (1) areas having low contrast (typicallylower than 7, though it can be adjusted according to the practicalapplication) compared to the surrounding areas, and (2) areas betweenthe proximal surfaces of adjacent teeth (hereafter referred to asinterproximal regions).

Low contrast false positives are removed by calculating the intensitycontrast between suspicious area and its surrounding area.

Interproximal false positives are removed according to the morphologicalfeatures of the suspicious caries located inside or connected to theinterproximal regions. To do this, the interproximal region is firstidentified.

A detailed description of how interproximal regions are located in teethimages according to the present invention is given below.

The image structure of interproximal regions in teeth images depends onhow close adjacent teeth are. To most effectively locate interproximalregions, it is helpful to first determine whether adjacent teeth in theidentified tooth regions are well separated. By “well separated” ismeant that image processing is capable of detecting an interproximaltooth region with reasonable probability. This can be done, for example,by examining the relative connectivity of the objects in the identifiedtooth regions in the image.

For adjacent teeth that are well separated, the interproximal regionscontain spaces that are part of the background. This first kind ofinterproximal region having clear demarcation of the adjacent teeth islocated as follows. Firstly, a distance transformation is applied to thebinary image of tooth regions, and the pixel with the largest distancemeasured from the boundaries of the identified tooth regions in thebinary image is located. Note that “largest distance” can be easilydetermined using any of a number of known algorithms. In the describedembodiment, the pixel lying within a tooth region that has the largestdistance to the boundary of the tooth region is most preferably chosen.However, any pixel with distance substantially close to the largestdistance (for example, preferably within 90% of the largest distance,less preferably within at least 80% of the largest distance) may also beused, as empirically determined to yield satisfactory results.Accordingly, throughout this application, “the pixel with the largestdistance” can be construed as “the pixel with distance substantiallyclose to the largest distance” in the manner just described. Secondly,the identified tooth region that is connected to the located pixel isassigned as one object, and the other identified tooth regions areassigned as another object. And thirdly, the pixels in the backgroundhaving substantially the same distance to each of the two objects arethen defined to be the interproximal regions. “The same distance” inthis application means that for two pixels, their respective distancesto the same feature differ by no more than a small number of pixels,preferably by no more than from 1 to 5 pixels, less preferably by nomore than about 10 pixels. This distance tolerance has been empiricallydetermined to yield satisfactory results. Accordingly, throughout thisapplication, the term “same distance” can be construed as “substantiallysame distance” for pixels in the manner just described.

For adjacent teeth that are very close to each other or adjoining, theinterproximal regions do not contain a clear demarcation of the adjacentteeth. Different image processing approaches have to be taken toidentify this second kind of interproximal region in tooth images. Inthe first inventive example, referring to FIGS. 4A through 4E, thesecond kind of interproximal regions are located in four steps withmarker-controlled watershed transformation and distance transformationin the region connected to the pixel with the largest distance.

FIG. 4A shows a binary image of a target tooth 165 a and two neighboringteeth 165 b and 165 c. The light areas represent the teeth, while thedark areas represent background of the teeth. The light and dark areasare separated by a boundary. The origin point 200 is defined as thepixel with the maximal distance to the boundaries of the teeth, thoughany point near the center of the target tooth 165 a can also be chosenas the origin point. The origin point can also be determined with othermethods according to practical applications. For example, if the toothlocated at the center of the image is chosen as the target tooth, thelocal maxima point closest to the image center can be selected as theorigin point. In the first step as shown in FIG. 4B, a fan of ray lines210 are cast from the origin point 200 outward in directions between 0°and 360°. Subsequently a contour line 202 is formed or defined frompoints at which each ray line 210 first encounters the boundary betweenthe light and dark areas.

In the second step, internal and external markers are identified ordetermined as follows. As shown in FIG. 4C, internal makers aredetermined from a certain circular area 222 around the origin point 200and the gray areas 220 a, 220 b, 220 c, 220 d. According to one exampleof the present invention, the radius of the circular area 222 is chosenas ¾ times of the maximal distance, the distance of the origin point 200to the tooth boundaries. The gray areas 220 a, 220 b, 220 c, 220 d areobtained by subtracting the area enclosed by the contour line 202 fromthe tooth areas 165 a, 165 b, 165 c, which have been determined by theboundary between the light and dark areas in reference to FIG. 4A. Theouter light areas 224, corresponding to the dark areas of FIG. 4A, aretaken as the external markers.

In the third step, a marker-controlled watershed transformation isapplied to a gradient image of a grayscale FIRE image with the abovedetermined internal and external markers. In one embodiment, thegrayscale FIRE image is generated from the green channel of the FIREimage, Ifgreen. In alternative embodiments, the grayscale FIRE image canbe generated from a mixing of the three channels using methods wellknown in the image processing art. This transformation results in awater basin 170 connected to the internal marker that corresponds to thecircular area 222 of FIG. 4C, and water basins 172 a, 172 b, 172 c, 172d connected to the internal markers that correspond to the gray areas220 a, 220 b, 220 c and 220 d of FIG. 4C, respectively. Thistransformation also results in watershed lines 173 a, 173 b. Watershedline 173 a separates water basins 172 a from 172 b, while watershed line173 b separates water basins 172 c from 172 d. As noted earlier, theterm “water basin”, also referred to as catchment basin, is a term ofthe marker-controlled watershed transformation art in imaging, known toa person skilled in the art.

In the fourth step, the pixels having the same distance to the twogroups of basins are then taken to be the second kind of interproximalregions. FIG. 4E shows parts of the interlines 176, indicating locationsof the interproximal regions that are identified. Interlines 176 areobtained by marker-controlled watershed transformation and distancetransformation. Region 174 a is obtained from water basin 170. Region174 b is obtained from a combination of water basins 172 a and 172 b. Aregion 174 c is obtained from a combination of water basins 172 c and172 d.

In the second inventive example, referring now to FIGS. 5A through 5E,the second kind of interproximal regions that have no clear demarcationare located in four steps with a different adaptation ofmarker-controlled watershed transformation and distance transformationin the region connected to the pixel with the largest distance. Althoughsharing similar third and fourth steps, this second inventive examplediffers from the first inventive example in the first two steps.

Similar to FIG. 4A, FIG. 5A shows a binary image of a target tooth 165 aand two neighboring teeth 165 b and 165 c. The light areas represent theteeth, while the dark areas represent background of the teeth.

In the first step as shown in FIG. 5B, a distance transformation isapplied to the image of FIG. 5A and results in a distance image Idist.Generally, the pixel value in a distance image represents the closestdistance of that pixel to the background of the teeth.

In the second step shown in FIG. 5C and FIG. 5D, the internal markers230 a, 230 b, 230 c and external marker 232 are determined as follows.

With Idist as the mask and Idist−dhome as the marker, usingmorphological grayscale reconstruction, a reconstructed image Idreconcan be obtained. Then Iseeds can be determined according to thefollowing equation:Iseeds=(Idrecon>Tdrecon)∩(Idist>Tdist),where Tdrecon and Tdist are two threshold values (for example,Tdrecon=5, and Tdist=10), respectively. The symbol (Idrecon>Tdrecon)refers to the area in which the pixel values of Idrecon are greater thanTdrecon, and the symbol (Idist>Tdist) refers to the area in which thepixel values of Idist are greater than Tdrecon. The symbol ∩ is theintersection operator, familiar to those skilled in set theory.

Seeded regions 230 a, 230 b, 230 c obtained from Iseeds are shown inFIG. 5C. In each seeded region, according to the distance image Idist inFIG. 5B, a seed point is identified as the pixel with maximal distance.For example, seed points 234 a, 234 b, and 234 c are the pixels havingmaximal distance in seeded areas 230 a, 230 b, and 230 c, respectively.Taking the seed point as the origin point and ¾ times of its distance asthe radius, for each seeded region, a circular region is created as aninternal marker corresponding to the seed point. Specifically, circularinternal markers 236 a, 236 b, and 236 c are created from seed points234 a, 234 b, and 234 c, respectively, as shown in FIG. 5D. Thebackground regions of the teeth are used as the external markers 232 a,232 b.

Similar to the third step of the first inventive example (in referenceto FIGS. 4A through 4E), in the third step, as shown in FIG. 5E,marker-controlled watershed transformation is applied to the gradientimage of a grayscale FIRE image with the above determined internalmarkers 236 a, 236 b, and 236 c and external markers 232 a, 232 b, andwater basin regions 238 a, 238 b, 238 c for internal markers 236 a, 236b, and 236 c are obtained, respectively. Finally, in the fourth step,again similar to the fourth step of the first inventive example,interlines 240 a, 240 b are located as the pixels having the samedistance to two neighboring water basin regions.

It is noted that the applicability of the method in this secondinventive example, as described with reference to FIGS. 5A through 5E,is not limited to interproximal regions with no clear demarcation of theadjacent teeth; it is also effective in locating interproximal regionswhere adjacent teeth are well separated. To apply this method,therefore, although it can be useful, it is not necessary to first carryout the step of determining whether adjacent teeth in the identifiedtooth regions are well separated or not.

After the interproximal regions are located, the suspicious caries areasconnected to the interproximal regions are then identified. Because sometrue caries are also located in these regions, not all the suspiciouscaries areas connected to the interproximal regions should be removed. Atrue caries often appears as a “grayscale hole”, which is an area ofdark pixels surrounded by lighter pixels in the grayscale image. Thus,the “grayscale hole” characteristic is used to test which of thesuspicious caries areas are true caries and should be retained, whilethe other suspicious areas connected to the interproximal regions areremoved as false positives.

After the false positives are removed, the remaining suspicious cariesareas are the extracted regions of caries 162. When displayed, theseareas may be outlined or highlighted with false colors in a displayedFIRE, fluorescence, or reflectance image of the teeth to aid cariesscreening or diagnosis. They are also used for caries quantificationanalysis, in the steps described below.

Step 130 of Finding a Sound Tooth Region Adjacent to the ExtractedLesion Area

Referring back to FIG. 3D, step 130 of identifying a sound tooth regionadjacent to the extracted lesion area is performed by expanding thesuspicious lesion areas 162 outward to dilated line 166 withmorphological dilation, an operation well known in the image processingart. This step is performed automatically without a need for user input.This step and steps 140 and 150 are preferably performed on thefluorescence image, for reasons explained below. The areas surroundingthe expanded suspicious lesion areas are taken as the normal/soundareas, and the values of the pixels making up the dilated line 166 aretaken as the intensity values of the surrounding normal/sound areas. Thealgorithmic implementation of the morphological dilation step is similarto that presented in FIG. 3 of commonly assigned co-pending U.S. PatentApplication Publication No. 2008/0170764. This step reduces errors evenif there are possible detection errors in the detected suspicious cariesregions and in the non-significant intensity changes in normal/soundtooth areas.

Step 140 of Reconstructing Intensity Values for Tooth Tissue within theLesion Area

For assessing the severity of the extracted lesions and for monitoringthe development of the identified lesions over time, it is helpful tohave an estimate of the normal intensity values of the suspicious cariesregions before the development of caries. This can be performed throughvarious approaches based on the intensity values of the surroundingnormal/sound areas found in Step 130.

In one embodiment, after the surrounding sound area is identified, thereconstructed intensity value for tooth tissue within the lesion areacan be obtained using a bilinear interpolation technique according tovalues in the adjacent sound region as described below.

FIG. 3E shows an exploded view of a region of interest 161 shown in FIG.3D. For each pixel P in the lesion area R 162, there are four pixels onthe dilated line 166 in the sound area that are to the left, right, top,and bottom of P, named P_(L), P_(R), P_(T), P_(B), respectively. Theestimation of the reconstructed intensity value I_(r) at P can becalculated using a bilinear interpolation, for which the formulae areshown below.

$I_{H} = \frac{{I_{L} \cdot x_{2}} + {I_{R} \cdot x_{1}}}{x_{2} + x_{1}}$$I_{V} = \frac{{I_{T} \cdot y_{2}} + {I_{B} \cdot y_{1}}}{y_{2} + y_{1}}$$I_{r} = \frac{I_{H} + I_{V}}{2}$

Bilinear interpolation is carried out in this way for every pixel in theregion of caries 162 to reconstruct the normal intensity values for thewhole region.

As an alternative embodiment, after the surrounding sound area isidentified, the reconstructed intensity value for tooth tissue withinthe lesion area can be obtained using a surface fitting technique suchas a two-dimensional spline, or Bézier fit.

Another alternative embodiment for reconstructing intensity value fortooth tissue within the lesion area is to smoothly interpolate inwardfrom the pixel's values on the boundaries of the expanded suspiciouscaries areas by solving Laplace's equation. This embodiment is anadaptation of a common image processing technique (such as what has beenimplemented in the familiar Matlab software function “roifill” in itsimage processing toolbox), and results in more accurate estimation.

Step 150 of Quantifying the Condition of the Caries

As discussed above, quantitative information on the regions of caries162 is helpful for assessing the condition of the extracted lesions andfor monitoring the development of the identified lesions over time. Thecondition of caries in a tooth image can be quantified in a number ofways, including calculating the size (or area) of the lesion area andcalculating fluorescence loss ratio of the lesion area.

In one example, the lesion area is calculated by counting the actualpixel number within the regions of caries 162, and then converting thatto actual spatial dimension, such as mm².

In another example, the fluorescence loss is used to measure thecondition of the caries. Fluorescence loss in tooth structure has beendemonstrated to be a direct indication of the degree of demineralizationin the structure. This quantity can be directly calculated from theintensity values in the tooth's fluorescence image. In the fluorescenceimage, the fluorescence loss ratio ΔF at each pixel within the lesionarea is calculated using the formula below:

${{\Delta\; F} = \frac{I_{r} - I_{o}}{I_{r}}},$where I_(r) is the reconstructed intensity value from step 140, andI_(o) is the actual measured intensity value of the green channel of thefluorescence image I_(Fluo). Where caries has occurred, ΔF>0.

The whole fluorescence loss L of the lesion region is the sum of ΔFwithin the lesion region R:

$L = {\sum\limits_{i \in R}{\Delta\; F_{i}}}$

FIG. 6A shows another method for quantification of caries using step120, which relies on the method for locating interproximal tooth regionsaccording to the present invention. This method for quantification ofcaries comprises a step of generating a FIRE image or other imagesobtained by combining a fluorescence image and a reflectance image ofthe tooth according to the present invention. FIG. 6A is similar toFIG. 1. However, in FIG. 6A the digital image of the tooth is a FIREimage or the like which is generated from both a reflectance image and afluorescence image. Particularly, the reflectance image is generatedusing white or single color light, while the fluorescence image isgenerated under excitation light in the ultraviolet-blue range. Duringstep 130 of identifying a sound region adjacent to the extracted lesionarea, the fluorescence image may substitute the FIRE image as input,indicated by the dashed arrow 160 a. During step 140 of reconstructingintensity values within a lesion area and step 150 of quantifying thecondition of the caries areas, the fluorescence image is also needed asinput, indicated by the arrow 160.

FIG. 6B shows yet another method for quantifying caries using step 120,which relies on the method for locating interproximal tooth regionsaccording to the present invention. It is similar to FIG. 6A, butdiffers in step 120 which specifically comprises steps of identifyingthe tooth regions 165 from a tooth image, extracting a suspicious lesionarea, and removing false positives. The dashed arrow 160 a shows thatthe fluorescence image may be used for step 130, and the arrow 160 showsthat the fluorescence image is used for steps 140 and 150.

Another alternative method for quantifying caries using step 120 relieson the method for locating interproximal tooth regions according to thepresent invention, Referring back to FIG. 1, the digital image generatedin step 110 is a fluorescence image of the tooth. As discussedpreviously, the fluorescence image has similar characteristics as theFIRE image, and so the methods used in the lesion areas extraction step120 can all be carried out on the fluorescence image. Therefore, in thisalternative embodiment, the fluorescence image is used in all steps fromStep 110 to Step 150.

The invention has been described in detail with particular reference toa presently preferred embodiment, but it will be understood thatvariations and modifications can be effected within the spirit and scopeof the invention. The presently disclosed embodiments are thereforeconsidered in all respects to be illustrative and not restrictive. Thescope of the invention is indicated by the appended claims, and allchanges that come within the meaning and range of equivalents thereofare intended to be embraced therein.

PARTS LIST

-   110 step of generating a digital image of a tooth-   120 step of extracting a lesion area from sound tooth regions-   130 step of identifying a sound region adjacent to the extracted    lesion area-   140 step of reconstructing intensity values for tooth tissue within    the lesion area-   150 step of calculating the condition of the caries-   160, 160 a arrow-   161 region of interest-   162 lesion area (or region of caries)-   163 segmentation border-   164 sound tooth area-   165 tooth region-   165 a, 165 b, 165 c tooth-   166 dilated line-   167 reflectance image-   168 fluorescence image-   169 FIRE image-   170 water basin corresponding to the circular internal marker area    222-   172 a, 172 b, 172 c, 172 d water basins-   173 a, 173 b watershed lines-   174 a region corresponding to water basin 170-   174 b region corresponding to a combination of water basins 172 a    and 172 b-   174 c region corresponding to a combination of water basins 172 c    and 172 d-   176 interlines between teeth-   180 processing apparatus-   200 origin point-   202 contour line-   210 ray lines-   220 a, 220 b, 220 c, 220 d gray areas corresponding to internal    markers-   222 circular area on the target tooth corresponding to internal    markers-   224 light areas corresponding to an external marker-   230 a, 230 b, 230 c seeded areas-   232 a, 232 b external markers-   234 a, 234 b, 234 c seed points-   236 a, 236 b, 236 c internal markers-   238 a, 238 b, 238 c water basin regions-   240 a, 240 b interlines

What is claimed is:
 1. A method for locating one or more interproximaltooth regions in a digital tooth image executed at least in part by aprocessor, comprising: accessing a fluorescence image of one or moreteeth; accessing a reflectance image of the one or more teeth;generating the digital tooth image from the fluorescence and reflectanceimages, the digital tooth image comprising intensity values for pixelscorresponding to a background and the one or more teeth; automaticallyidentifying one or more tooth regions by processing the pixels in thedigital tooth image; and automatically locating the one or moreinterproximal tooth regions according to the one or more identifiedtooth regions; wherein locating the one or more interproximal toothregions comprises; determining internal markers in the tooth regions andexternal markers in the background according to a binary image of thedigital tooth image; applying a marker-controlled watershedtransformation to a gradient image of a grayscale version of the digitaltooth image with the internal and external markers to form two groups ofwater basins; and identifying at least a first interproximal toothregion as those pixels having substantially the same distance to thefirst and second groups of water basins.
 2. The method of claim 1further comprising determining whether adjacent teeth in the one or moreidentified tooth regions are separated.
 3. The method of claim 1,wherein locating the one or more interproximal tooth regions comprises:applying a distance transformation to a binary image of the digitaltooth image to locate a first pixel wherein the first pixel lies withina first tooth region and wherein the first pixel has a largest distancefrom the boundaries of the first tooth region; assigning the first toothregion that is connected to the located first pixel as a first object;assigning a second tooth region that is not connected to the locatedfirst pixel as a second object; and identifying a first interproximalregion to be the pixels in the background having substantially the samedistance to the first and second objects.
 4. The method of claim 1,wherein locating the one or more interproximal tooth regions comprises:defining an origin point in a target tooth in a binary image of thedigital tooth image; casting a fan of ray lines outward from the originpoint in the target tooth in a plurality of angles; defining a contourline at points at which each ray line first encounters a boundarybetween tooth and background areas, determining internal markers in thetooth regions and external markers in the background from within thebinary image; applying a marker-controlled watershed transformation to agradient image of a grayscale version of the digital tooth image withthe internal and external markers to form first and second groups ofwater basins; and identifying at least a first interproximal toothregion as those pixels having substantially the same distance to thefirst and second groups of water basins.
 5. The method of claim 1,wherein locating the one or more interproximal tooth regions comprises:determining markers for a targeted tooth, one or more neighboring teeth,and background from the digital tooth image; applying amarker-controlled watershed transformation to a gradient image of agrayscale version of the digital tooth image with the markers to formtwo groups of water basins; and identifying at least a firstinterproximal tooth region as those pixels having substantially the samedistance to the two groups of water basins.
 6. The method of claim 1further comprising displaying at least a first interproximal region. 7.The method of claim 1 further comprising: storing in a memory,transmitting using the processor, or displaying using a display, thelocated one or more interproximal tooth regions.
 8. A method forlocating one or more interproximal regions in a digital tooth imageexecuted at least in part by a processor wherein adjacent teeth have noclear demarcation, comprising: defining an origin point in a targettooth in a binary image of the digital tooth image; casting a fan of raylines outward from the origin point in the target tooth in a pluralityof angles in the digital tooth image; defining a contour line at pointsat which each ray line first encounters a boundary between tooth andbackground areas in the digital tooth image; determining internalmarkers in the tooth areas and external markers in the background areasfrom the binary image of the digital tooth image; applying amarker-controlled watershed transformation to a gradient image of agrayscale version of the digital tooth image with the internal andexternal markers to form first and second groups of water basins; andautomatically identifying at least a first interproximal region in thedigital tooth image as those pixels having substantially the samedistance to the first and second groups of water basins by saidprocessor.
 9. The method of claim 8 wherein the digital tooth image is afluorescence image.
 10. The method of claim 8 wherein the digital toothimage is obtained by combining image data for fluorescence andreflectance images of the tooth.
 11. The method claim 8 furthercomprising: storing, transmitting, or displaying the identified at leastfirst interproximal region in the digital tooth image.
 12. A method forlocating one or more interproximal regions in a 2D digital tooth imageexecuted at least in part by a processor, comprising: applying adistance transformation to a binary image of the digital tooth image, toform a distance image, the binary image of the 2D digital tooth imageincluding background regions and one or more tooth regions, where apixel value in the distance image represents a distance of that pixel tothe background regions; determining internal and external markers in the2D digital tooth image using the distance image; applying amarker-controlled watershed transformation to a gradient image of agrayscale version of the 2D digital tooth image combined with theinternal and external markers to form two groups of water basins; andautomatically identifying at least a first interproximal region in the2D digital tooth image as those pixels having substantially the samedistance to the two groups of water basins by said processor.
 13. Themethod of claim 12 wherein the digital tooth image is a fluorescenceimage.
 14. The method of claim 12 wherein the digital tooth image isobtained by combining image data for fluorescence and reflectance imagesof the tooth.
 15. The method claim 12 further comprising: storing,transmitting, or displaying the identified at least first interproximalregion in the digital tooth image.